Abstract
Cellular neural network (CNN) has been acted as a high-speed parallel analog signal processor gradually. However, recently, since the decrease in the size of transistor is going to approach the utmost, the transistor-based integrated circuit technology hits a bottleneck. As a result, the advantage of very large scale integration implementation of CNN becomes hard to really present, and further development of this era faces severe challenges unavoidably. In this study, two types of memristor-based cellular neural networks have been proposed. One type uses a memristor to replace the linear resistor in a conventional CNN cell circuit. And the other places a resonant tunneling diode (RTD) in this position and uses memristive synaptic connections to structure a hybrid memristor RTD CNN model. The excellent performances of the proposed CNNs are verified by conventional means of, for instance, stability analysis and efficient applications in image processing. Since both the memristor and the resonant tunneling diode are nanoscale, the size of the network circuits can be greatly reduced, and the integration density of the system will be significantly improved.
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Acknowledgments
The work was supported by Program for New Century Excellent Talents in University, National Natural Science Foundation of China (Grant Nos. 61372139, 61101233, 60972155), “Spring Sunshine Plan” Research Project of Ministry of Education of China (Grant No. z2011148), Technology Foundation for Selected Overseas Chinese Scholars, Ministry of Personnel in China (Grant No. 2012-186), University Excellent Talents Supporting Foundations in of Chongqing (Grant No. 2011-65), University Key Teacher Supporting Foundations of Chongqing (Grant No. 2011-65), Fundamental Research Funds for the Central Universities (Grant Nos. XDJK2012A007, XDJK2013B011).
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Duan, S., Hu, X., Wang, L. et al. Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing. Neural Comput & Applic 25, 291–296 (2014). https://doi.org/10.1007/s00521-013-1484-x
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DOI: https://doi.org/10.1007/s00521-013-1484-x